@book{bps93,
author = {Kenneth D. Forbus and Johan De Kleer},
title = {Building Problem Solvers},
year = {1993},
isbn = {0262560720},
publisher = {MIT Press},
address = {Cambridge, MA, USA},
}

@book{suppes57,
author = {Patrick Suppes},
title = {Introduction to Logic},
year = {1957},
publisher = {D. Van Nostrand Co.},
address = {Princeton, NJ, USA},
}

@article{kumar2007,
	author = {Kumar, Vinay  S.  and Dasika, Madhukar  S.  and Maranas, Costas  D. },
	citeulike-article-id = {1403526},
	doi = {10.1186/1471-2105-8-212},
	issn = {1471-2105},
	journal = {BMC Bioinformatics},
	keywords = {biohacker, fba, metabolic-reconstruction},
	month = {June},
	pages = {212+},
	posted-at = {2008-05-14 16:32:50},
	priority = {2},
	title = {Optimization based automated curation of metabolic reconstructions},
	url = {http://dx.doi.org/10.1186/1471-2105-8-212},
	volume = {8},
	year = {2007}
}

@article{McShan2004,
	abstract = {We present a new symbolic computational approach to elucidate the biochemical networks of living systems de novo and we apply it to an important biomedical problem: xenobiotic metabolism. A crucial issue in analyzing and modeling a living organism is understanding its biochemical network beyond what is already known. Our objective is to use the available metabolic information in a representational framework that enables the inference of novel biochemical knowledge and whose results can be validated experimentally. We describe a symbolic computational approach consisting of two parts. First, biotransformation rules are inferred from the molecular graphs of compounds in enzyme-catalyzed reactions. Second, these rules are recursively applied to different compounds to generate novel metabolic networks, containing new biotransformations and new metabolites. Using data for 456 generic reactions and 825 generic compounds from KEGG we were able to extract 110 biotransformation rules, which generalize a subset of known biocatalytic functions. We tested our approach by applying these rules to ethanol, a common substance of abuse and to furfuryl alcohol, a xenobiotic organic solvent, which is absent in metabolic databases. In both cases our predictions on the fate of ethanol and furfuryl alcohol are consistent with the literature on the metabolism of these compounds.},
	address = {School of Medicine, University of Colorado, 4200 East 9th Avenue, B-119, Denver, CO 80262, USA. daniel.mcshan@uchsc.edu},
	author = {McShan, D. C.  and Updadhayaya, M.  and Shah, I. },
	citeulike-article-id = {2799159},
	issn = {1793-5091},
	journal = {Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing},
	keywords = {biohacker, cheminformatics, symbolic-programming},
	pages = {545--556},
	posted-at = {2008-05-14 16:18:15},
	priority = {2},
	title = {Symbolic inference of xenobiotic metabolism.},
	url = {http://view.ncbi.nlm.nih.gov/pubmed/14992532},
	year = {2004}
}



@article{ecocyc,
	abstract = {The EcoCyc database (http://EcoCyc.org/) is a comprehensive source of information on the biology of the prototypical model organism Escherichia coli K12. The mission for EcoCyc is to contain both computable descriptions of, and detailed comments describing, all genes, proteins, pathways and molecular interactions in E.coli. Through ongoing manual curation, extensive information such as summary comments, regulatory information, literature citations and evidence types has been extracted from 8862 publications and added to Version 8.5 of the EcoCyc database. The EcoCyc database can be accessed through a World Wide Web interface, while the downloadable Pathway Tools software and data files enable computational exploration of the data and provide enhanced querying capabilities that web interfaces cannot support. For example, EcoCyc contains carefully curated information that can be used as training sets for bioinformatics prediction of entities such as promoters, operons, genetic networks, transcription factor binding sites, metabolic pathways, functionally related genes, protein complexes and protein-ligand interactions.},
	address = {SRI International, 333 Ravenswood Avenue, Menlo Park, CA 94025, USA.},
	author = {Keseler, I. M.  and Collado-Vides, J.  and Gama-Castro, S.  and Ingraham, J.  and Paley, S.  and Paulsen, I. T.  and Peralta-Gil, M.  and Karp, P. D. },
	citeulike-article-id = {525397},
	issn = {1362-4962},
	journal = {Nucleic Acids Res},
	keywords = {biocyc, biohacker, ecocyc},
	month = {January},
	number = {Database issue},
	posted-at = {2006-09-20 02:36:10},
	priority = {0},
	title = {EcoCyc: a comprehensive database resource for Escherichia coli.},
	url = {http://view.ncbi.nlm.nih.gov/pubmed/15608210},
	volume = {33},
	year = {2005}
}




@article{joyce2006,
	abstract = {Genome-wide gene essentiality data sets are becoming available for Escherichia coli, but these data sets have yet to be analyzed in the context of a genome scale model. Here, we present an integrative model-driven analysis of the Keio E. coli mutant collection screened in this study on glycerol-supplemented minimal medium. Out of 3,888 single-deletion mutants tested, 119 mutants were unable to grow on glycerol minimal medium. These conditionally essential genes were then evaluated using a genome scale metabolic and transcriptional-regulatory model of E. coli, and it was found that the model made the correct prediction in [~]91\% of the cases. The discrepancies between model predictions and experimental results were analyzed in detail to indicate where model improvements could be made or where the current literature lacks an explanation for the observed phenotypes. The identified set of essential genes and their model-based analysis indicates that our current understanding of the roles these essential genes play is relatively clear and complete. Furthermore, by analyzing the data set in terms of metabolic subsystems across multiple genomes, we can project which metabolic pathways are likely to play equally important roles in other organisms. Overall, this work establishes a paradigm that will drive model enhancement while simultaneously generating hypotheses that will ultimately lead to a better understanding of the organism. 10.1128/JB.00740-06},
	author = {Joyce, Andrew  R.  and Reed, Jennifer  L.  and White, Aprilfawn   and Edwards, Robert   and Osterman, Andrei   and Baba, Tomoya   and Mori, Hirotada   and Lesely, Scott  A.  and Palsson, Bernhard  O.  and Agarwalla, Sanjay  },
	citeulike-article-id = {2179203},
	doi = {10.1128/JB.00740-06},
	journal = {J. Bacteriol.},
	keywords = {biohacker, ecoli, fba, metabolic-models, palsson},
	month = {December},
	number = {23},
	pages = {8259--8271},
	posted-at = {2008-05-14 16:27:54},
	priority = {2},
	title = {Experimental and Computational Assessment of Conditionally Essential Genes in Escherichia coli},
	url = {http://dx.doi.org/10.1128/JB.00740-06},
	volume = {188},
	year = {2006}
}



@article{covert2004,
	abstract = {The flood of high-throughput biological data has led to the expectation that computational (or in silico) models can be used to direct biological discovery, enabling biologists to reconcile heterogeneous data types, find inconsistencies and systematically generate hypotheses. Such a process is fundamentally iterative, where each iteration involves making model predictions, obtaining experimental data, reconciling the predicted outcomes with experimental ones, and using discrepancies to update the in silico model. Here we have reconstructed, on the basis of information derived from literature and databases, the first integrated genome-scale computational model of a transcriptional regulatory and metabolic network. The model accounts for 1,010 genes in Escherichia coli, including 104 regulatory genes whose products together with other stimuli regulate the expression of 479 of the 906 genes in the reconstructed metabolic network. This model is able not only to predict the outcomes of high-throughput growth phenotyping and gene expression experiments, but also to indicate knowledge gaps and identify previously unknown components and interactions in the regulatory and metabolic networks. We find that a systems biology approach that combines genome-scale experimentation and computation can systematically generate hypotheses on the basis of disparate data sources.},
	address = {Bioengineering Department, University of California, San Diego, 9500 Gilman Drive, La Jolla, California 92093-0412, USA.},
	author = {Covert, M. W.  and Knight, E. M.  and Reed, J. L.  and Herrgard, M. J.  and Palsson, B. O. },
	citeulike-article-id = {323024},
	doi = {10.1038/nature02456},
	issn = {1476-4687},
	journal = {Nature},
	keywords = {biohacker, fba, metabolic-models, metabolic-reconstruction},
	month = {May},
	number = {6987},
	pages = {92--96},
	posted-at = {2008-05-14 16:26:30},
	priority = {2},
	title = {Integrating high-throughput and computational data elucidates bacterial networks.},
	url = {http://dx.doi.org/10.1038/nature02456},
	volume = {429},
	year = {2004}
}

@book{HACKER,
 author = {Gerald Jay Sussman},
 title = {A  Computer Model of Skill Acquisition},
 year = {1975},
 isbn = {044400159X},
 publisher = {Elsevier Science Inc.},
 address = {New York, NY, USA},
 }

@article{romero2001,
	abstract = {We present an algorithm that solves two related problems in the analysis of metabolic networks stored within a pathway/genome database. (1) The Forward Propagation Problem: given a set of nutrients that are inputs to the metabolic network, what compounds will be produced by the metabolic network? (2) The Backtracking Problem: given the results of a forward propagation, and given a set of essential compounds that are not produced as a result of the forward propagation, what precursors must be supplied to produce those essential compounds? A program based on this algorithm is applied to the EcoCyc database, which is a pathway/genome database for E. coli that consists of annotated genomes and the metabolic reactions and pathways associated with the known gene products. The inputs to the program are a description of the metabolic network of an organism (EcoCyc), a set of nutrients corresponding to a known minimal growth medium, and a list of essential compounds to be produced. The program "fires" the microorganism's metabolism contained in the database and predicts all synthesized and nonsynthesized essential compounds, along with the missing precursors required to produce the latter. When applied to the EcoCyc database, the program identifies a number of missing precursors that indicate incomplete regions of the database. Thus the program results can be used to evaluate existing pathway databases like EcoCyc.},
	address = {Artificial Intelligence Center, SRI International, 333 Ravenswood Ave., Menlo Park, CA 94025, USA. promero@ai.sri.com},
	author = {Romero, P. R.  and Karp, P. },
	citeulike-article-id = {2799327},
	issn = {1793-5091},
	journal = {Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing},
	keywords = {biohacker, ecocyc, symbolic-programming},
	pages = {471--482},
	posted-at = {2008-05-14 16:46:41},
	priority = {2},
	title = {Nutrient-related analysis of pathway/genome databases.},
	url = {http://view.ncbi.nlm.nih.gov/pubmed/11262965},
	year = {2001}
}

	
@article{king2004,
	abstract = {The question of whether it is possible to automate the scientific process is of both great theoretical interest and increasing practical importance because, in many scientific areas, data are being generated much faster than they can be effectively analysed. We describe a physically implemented robotic system that applies techniques from artificial intelligence to carry out cycles of scientific experimentation. The system automatically originates hypotheses to explain observations, devises experiments to test these hypotheses, physically runs the experiments using a laboratory robot, interprets the results to falsify hypotheses inconsistent with the data, and then repeats the cycle. Here we apply the system to the determination of gene function using deletion mutants of yeast (Saccharomyces cerevisiae) and auxotrophic growth experiments. We built and tested a detailed logical model (involving genes, proteins and metabolites) of the aromatic amino acid synthesis pathway. In biological experiments that automatically reconstruct parts of this model, we show that an intelligent experiment selection strategy is competitive with human performance and significantly outperforms, with a cost decrease of 3-fold and 100-fold (respectively), both cheapest and random-experiment selection.},
	address = {Department of Computer Science, University of Wales, Aberystwyth SY23 3DB, UK.},
	author = {King, R. D.  and Whelan, K. E.  and Jones, F. M.  and Reiser, P. G.  and Bryant, C. H.  and Muggleton, S. H.  and Kell, D. B.  and Oliver, S. G. },
	citeulike-article-id = {292},
	doi = {10.1038/nature02236},
	issn = {1476-4687},
	journal = {Nature},
	keywords = {agi, ai},
	month = {January},
	number = {6971},
	pages = {247--252},
	posted-at = {2007-08-11 01:19:38},
	priority = {3},
	title = {Functional genomic hypothesis generation and experimentation by a robot scientist.},
	url = {http://dx.doi.org/10.1038/nature02236},
	volume = {427},
	year = {2004}
}

	
